Developed and implemented a concept for credit beta; construction of a beta neutral long/short credit portfolio

Quantitative models for, for example, hedge fund risk factors; predicting high yield corporate bond spreads; a rating model for corporates based on discriminant analysis; a tool for allocation between government and corporate bonds; asset allocation in an absolute return framework

Investigation of how far financial advisors‘ recommendations are away from the efficient frontier (paper published as Huber, C., Kaiser, H. (2003): Asset Allocation Recommendations of Financial Advisors: Are They Risk/Return Optimal?, see list of publications.

Developed the selection process for a portfolio of Liquid Alternatives utilising Self-Organising Maps

Artificial Intelligence and Machine Learning are all the buzz these days. This presentation describes the application of a method of Machine Learning, the Self-Organising Maps, to build robust hedge fund portfolios (for a quick overview see slides 3 and 14). A variant of this approach was adopted by TradeCap AG, a Swiss investment manager, to build their portfolio of Liquid Alternatives. TradeCap recently received seed commitment of USD 100 mln from a Swiss institutional investor.